Developed a user-configurable prompt token truncation feature for the jeejeelee/vllm repository, enabling greater flexibility and efficiency when interacting with OpenAI endpoints. The solution introduced new parameters in the request model and updated rendering logic to allow users to select whether tokens are trimmed from the left or right, directly addressing token waste in chat completions. Leveraged Python for backend and API development, with a focus on robust testing to ensure safe defaults and reliable integration. Collaborated closely with peers through code review and co-authored commits, demonstrating a disciplined approach to quality and maintainability throughout the development process.